Gene mining system and method

ABSTRACT

The present invention provides a system, method and apparatus for targeting gene sequences having one or more phenotypic characteristics using a computer. One or more phenotypic characteristics are selected. A gene sequence is then selected that is known to have the selected phenotypic characteristics. In addition, one or more databases containing cataloged gene sequences are selected. The selected gene sequence is compared to the cataloged gene sequences, and any cataloged gene sequences that contain a portion of the selected gene sequence are extracted. The selected gene sequence is aligned to each portion of the extracted gene sequence and the extracted gene sequences are prioritized based on the alignment of the selected gene sequence. At least one of the prioritized gene sequences is selected based on one or more phenotypic criteria. Finally, one or more degenerate primers are designed to target the selected-prioritized gene sequences.

CROSS-REFERENCE TO RELATED APPLICATIONS

This Application for Patent is a continuation of U.S. patent applicationSer. No. 09/696,801, filed 25 Oct. 2000, and now allowed; which claimsthe benefit of priority from, U.S. Provisional Application for PatentSer. No. 60/161,527, filed 26 Oct. 1999; and Ser. No. 60/161,571, filed26 Oct. 1999. Each of these applications is incorporated in its entiretyherein by reference.

TECHNICAL FIELD

This invention relates to the targeted isolation of biologically andfunctionally relevant gene and genomic information and bioinformaticsand more particularly to a system, method and apparatus for targetingand cloning gene sequences based on functional observations from datamined from available gene databases.

BACKGROUND ART

Without limiting the scope of the invention, its background is describedin connection with uses of functional genomics and bioinformatics, as anexample.

The present invention relates generally to methods and systems forsearching and identifying functional nucleic acid sequences and proteinsencoded by genes available from the multitude of nucleic acid andprotein databases presently available. These biological databases storeinformation that is searchable and from which biological information maybe retrieved. More particularly, the present invention relates tosystems and methods for identifying biologically relevant sequences ofbiological molecules using an integrated approach that specificallyidentifies sequences for cloning.

Generally, informatics may be defined as the study and application ofcomputer and statistical techniques to the management of information. Inprojects related to biological information, the term “bioinformatics”has been coined to include the development of methods to, e.g., searchdatabases, analyze nucleic acid sequence information, predict proteinsequence, protein structure, and protein function from nucleic acidsequence data.

The widespread use and availability of molecular biological techniqueshave allowed for the rapid development and identification of nucleicacid derived sequences. With the widespread availability of advancedcomputer systems and the integration of laboratory equipment withcomputer software, researchers are able to conduct advanced quantitativeanalyses, database comparisons and computational algorithms to seek andidentify gene sequences with homology to known sequences.

Examples of large-scale sequencing and the availability of geneticinformation for a number of organisms have been cataloged in a number ofpublic and private computer databases. Genetic databases for organismssuch as Escherichia coli, Haemophilus influenzae, Mycoplasma genitalium,and Mycoplasma pneumoniae, to name a few, are publicly available. Atpresent, however, complete sequence data is available for relatively fewspecies, and the ability to manipulate sequence data within and betweenspecies and databases is greatly limited by the ability of these publicdatabases to be searched for functional significance.

One example of a system for comparing relational databases of sequencesis disclosed in U.S. Pat. No. 5,966,712, issued to Sabatini, et al. Thesystem disclosed is a relational database system for storing andmanipulating biomolecular sequence information and includes a databaseof genomic libraries for a plurality of types of organisms. Theselibraries are taught to have multiple genomic sequences, at least someof which represent open reading frames located along a contiguoussequence in each of the plurality of organisms' genomes. A userinterface is provided and is capable of receiving a selection of two ormore of the genomic libraries for comparison and displaying the resultsof the comparison. The system also provides a user interface capable ofreceiving a selection of one or more probe open reading frames for usein determining homologous matches between such probe open readingframe(s) and the open reading frames in the genomic libraries, anddisplaying the results of the determination.

Also needed are fully integrated systems that take advantage offunctional observations and the identification of biologically relevantand functional gene sequences. This disconnect between genotype andphenotype leads to the pursuit of many genes of doubtful relevance oreven mere artifacts. Thus, researchers are presently unable to avoidusing available computer resources to explore, identify and studyrelevant gene sequences, gene expression, and molecular structurewithout extensive experimentation.

Another such use of bioinformatics involves studying an organism'sgenome to determine the sequence and placement of its genes and theirrelationship to other sequences and genes within the genome or to genesin other organisms. The study of the relationship between introns andexons, for example across species, allows for a scientific understandingof many underlying substructures of the protein or proteins beingexpressed. It also allows for the identification of sequences that areinvolved in the regulation of the gene or genes that are at a particulargene locus. Such information may be of significant interest inbiomedical and pharmaceutical research to assist in the evaluation ofpotential drug efficacy and resistance for genes that are well studiedand for which significant structure-function studies have beenconducted. In one such database system (Incyte Pharmaceuticals, Inc.,U.S.A.), software has been developed that searched the annotatedinformation that is part of genomic sequence data in publicly availablesequence databases. Unfortunately, not all electronically recordedsequences contain annotated information. Some contain information thatis not functional, contain information that is not accurate, or containinformation that has no relation to function. Examples of such databasesinclude the widely available public databases GenBank (NCBI) and TIGR.Therefore, the accuracy and relevance of any search results from thesedatabases often has no bearing on the cellular biological function of aparticular protein of gene regulatory element.

Although genetic data processing and relational database systems such asthose developed by Incyte Pharmaceuticals, Inc. provide great power andflexibility in analyzing genetic information, this area of technology isstill in its infancy and further improvements in genetic data processingand relational database systems will help accelerate biological researchfor numerous applications.

DISCLOSURE OF THE INVENTION

While publicly available databases make manipulation of gene and genomicinformation easy to perform and understand, sophisticated computerdatabase systems have not been developed that begin their searchingbased on functional biologically-relevant information. Furthermore, aneed has been recognized for the identification, isolation and cloningof biologically relevant genes and genomic information mined fromavailable resources. While large amounts of sequence data are beinggenerated as part of the Human Genome Project and other like projects, acoordinated system and method for culling functionally relevantsequences is needed. Also needed are systems and methods for mininggenes based on the observation of biologic data, for which anunderstanding of the genetic basis for the observation is known orunknown.

The present invention provides a method for targeting gene sequenceshaving one or more genotypic or phenotypic characteristics using acomputer. One or more genotypic or phenotypic characteristics areselected. A gene sequence is then selected that is known to have theselected phenotypic characteristics. In addition one or more databasescontaining cataloged gene sequences are selected. The selected genesequence is compared to the cataloged gene sequences, and any catalogedgene sequences that contain a portion of the selected gene sequence areextracted. The selected gene sequence is aligned to each portion of theextracted gene sequence and the extracted gene sequences are prioritizedbased on the alignment of the selected gene sequence. At least one ofthe prioritized gene sequences is selected based on one or morephenotypic criteria. Finally, one or more degenerate primers aredesigned to target the selected-prioritized gene sequences.

The present invention also provides a computer program embodied on acomputer-readable medium that performs the steps described above. Inaddition, the present invention provides a system having a computer, oneor more databases containing the cataloged gene sequences, and acommunication link connecting the computer to the one or more databases.The computer is used to select one or more phenotypic characteristics,select a gene sequence that is known to have the selected phenotypiccharacteristics, compare the selected gene sequence to the catalogedgene sequences, extract any cataloged gene sequences that contain aportion of the selected gene sequence, align the selected gene sequenceto each portion of the extracted gene sequence, prioritize the extractedgene sequences based on the alignment of the selected gene sequence,select at least one of the prioritized gene sequences based on one ormore phenotypic criteria, and design one or more degenerate primers totarget the selected-prioritized gene sequences.

Thus, the present invention takes the current state of the art, whichrequires combing GenBank with individual sequences to discover all ofthe homologous sequence, to a fully automated system that includes notonly sequence parameters in the search, but includes other searchparameters like species, protein characteristics and functional domains.Further, multiple homology search algorithms are seamlessly incorporatedinto the method. This not only allows nucleotide or amino acid searchesto be performed, but allows any conceivable type of search algorithm tobe employed without requiring the user to do more than select thedesired parameters. In this way, multiple types of databases (e.g.,nucleotide, amino acid, 3D structure, etc.) can be searched, evensimultaneously if desired.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the features and advantages of thepresent invention, reference is now made to the detailed description ofthe invention along with the accompanying figures in which correspondingnumerals in the different figures refer to corresponding parts and inwhich:

FIG. 1 is a block diagram showing some features of the presentinvention;

FIG. 2 is a basic flow chart showing a gene sequence targeting programin accordance with the present invention;

FIG. 3 is a flow chart showing the phenotypic characteristic selectionprocess in accordance with the present invention;

FIG. 4 is a flow chart showing the gene sequence selection process inaccordance with the present invention;

FIG. 5 is a flow chart showing the database selection process inaccordance with the present invention;

FIG. 6 provides the system network overview in the SPADE™ system;

FIG. 7 provides the program flow in the SPADE™ system;

FIG. 8 provides the database management screen in the SPADE™ system;

FIG. 9 provides the workspace management screen in the SPADE™ system;

FIG. 10 provides the search analysis tools screen in the SPADE™ system;

FIG. 11 provides the system architecture overview of the SPADE™ system;

FIG. 12 provides an example of an application of the SPADE™ system;

MODES OF CARRYING OUT THE INVENTION

The present invention will now be described more fully hereinafter withreference to the accompanying drawings, in which preferred embodimentsof the invention are shown. This invention may, however, be embodied inmany different forms and should not be construed as limited to theembodiments set forth herein; rather, these embodiments are provided sothat this disclosure will be thorough and complete, and will fullyconvey the scope of the invention to those skilled in the art.

While the making and using of various embodiments of the presentinvention are discussed in detail below, it should be appreciated thatthe present invention provides many applicable inventive concepts thatmay be embodied in a wide variety of specific contexts. The specificembodiments discussed herein are merely illustrative of specific ways tomake and use the invention and do not delimit the scope of theinvention.

Definitions

As used throughout the present specification the following abbreviationsare used: TF, transcription factor; ORF, open reading frame; kb,kilobase (pairs); UTR, untranslated region; kD, kilo Dalton; PCR,polymerase chain reaction; RT, reverse transcriptase.

The term “x % homology” refers to the extent to which two nucleic acidor protein sequences are complementary as determined by BLAST homologyalignment as described by T. A. Tatusova & T. L. Madden (1999), “Blast 2sequences—a new tool for comparing protein and nucleotide sequences”,FEMS MICROBIOL LETT. 174:247-250 and using the following parameters:Program (blastn) or (blastp) as appropriate; matrix (OBLOSUM62), rewardfor match (1); penalty for mismatch (−2); open gap (5) and extension gap(2) penalties; gap x-drop off (50); Expect (10); word size (11); filter(off). An example of a web based two sequence alignment program usingthese parameters is found at the world wide web addressncbi.nlm.nih.gov/gorf/bl2.html.

Tools

Alignment tools for use with the present invention may include, e.g.,BLAST. BLAST (Basic Local Alignment Search Tool) is a heuristic searchalgorithm employed by the programs blastp, blastn, blastx, tblastn, andtblastx. This combination of programs use the statistical methods ofKarlin and Altschul (1990, 1993). More recent versions of the programallow for tailoring of the sequence similarity during a searching, e.g.,to identify homologs in a query sequence. The programs are not generallyuseful for motif-style searching.

The fundamental unit of BLAST algorithm output is the High-scoringSegment Pair (HSP). An HSP includes two sequence fragments of arbitrarybut equal length whose alignment is locally maximal and for which thealignment score meets or exceeds a threshold or cutoff score. A set ofHSPs is thus defined by two sequences, a scoring system, and a cutoffscore. This HSP set may be empty if the cutoff score is sufficientlyhigh. In the software implementation of the BLAST algorithm, each HSPhas a segment from the query sequence and one from a database sequence.The sensitivity and speed of the programs may be adjusted using thestandard BLAST algorithm parameters W, T, and X (Altschul, et al.,1990). Furthermore, the selectivity of the programs may be adjusted viathe cutoff score.

The approach to similarity searching taken by the BLAST programs isfirst to look for similar segments (HSPs) between the query sequence anda database sequence. Next, the statistical significance of any matchesthat were found is evaluated. Finally, those matches that satisfy auser-selectable threshold of significance are reported. The finding ofmultiple HSPs involving the query sequence and a single databasesequence are treated statistically in a variety of ways. Another problemwith standard BLAST is that it uses the default programs devised for“Sum” statistics (Karlin and Altschul, 1993), as such, the statisticalsignificance ascribed to a set of HSPs may be higher than that of anyindividual member of the set. Only when the ascribed significancesatisfies the user-selectable threshold will the match be reported tothe user.

The task of finding HSPs begins by identifying short words of length Win a query sequence that either match or satisfy some positive-valuedthreshold score T when aligned with a word of the same length in adatabase sequence. The identification of the first short word as alocation to initiate a search is one of the limitations of the BLASTsearch, as it identifies a first location to initiate an alignment andanchors its alignment at that location. By prefiltering sequences suchthat irrelevant sequences are removed, a priori, even the BLASTalignment tool may be used with the present invention. Furthermore, byprefiltering the search sequences, open database BLAST searching is mademore efficient by limiting search parameters to those that arefunctional rather than artifactual. Removal of artifactual sequencesfrom the potential search pool further aids in the location of relevantgenes due to the limit of search results imposed by BLAST to 50potential sequences. T is referred to as the neighborhood word scorethreshold (Altschul, et al., 1990). These initial neighborhood word hitsact as seeds for initiating searches to find longer HSPs containingthem. The word hits are extended in both directions along each sequencefor as far as the cumulative alignment score may be increased. Extensionof the word hits in each direction are halted when: the cumulativealignment score falls off by the quantity X from its maximum achievedvalue; the cumulative score goes to zero or below, due to theaccumulation of one or more negative-scoring residue alignments; or theend of either sequence is reached.

A Maximal-scoring Segment Pair (MSP) is defined by two sequences and ascoring system and is the highest-scoring of all possible segment pairsthat can be produced from the two sequences. The statistical methodsdescribed by Karlin and Altschul (1990, 1993) may be used to determinethe significance of MSP scores in the limit of long sequences, under arandom sequence model that assumes independent and identicallydistributed choices for the residues at each position in the sequences.These statistics may be modified by the filtering of the presentinvention to the task of assessing the significance of HSP scoresobtained from comparisons of prefiltered potentially short, biologicalsequences.

The five BLAST programs described here perform the following tasks:blastp compares an amino acid query sequence against a protein sequencedatabase; blastn compares a nucleotide query sequence against anucleotide sequence database; blastx compares the six-frame conceptualtranslation products of a nucleotide query sequence (both strands)against a protein sequence database; and tblastn compares a proteinquery sequence against a nucleotide sequence database dynamicallytranslated in all six reading frames, also for both strands. Moreparticularly, tblastx compares the six-frame translations of anucleotide search query sequence against the six-frame translations of anucleotide sequence database.

BLAST restricts the number of short descriptions of matching sequencesreported to the number specified; default limit is 100 descriptions.During the alignment procedure, BLAST restricts database sequences tothe number of specified high-scoring segment pairs (HSPs) that arerequested and thereby limits its reporting function. The default HSPlimit is 50. If more than 50 database sequences satisfy the statisticalsignificance threshold for reporting, BLAST only matches and reportsthose sequences given the greatest statistical significance.

The statistical significance threshold (EXCEPT value) for reportingmatches against database sequences is 10, such that 10 matches areexpected to be found merely by chance, according to the stochastic modelof Karlin and Altschul (1990). If the statistical significance ascribedto a match is greater than the EXPECT threshold, the match will not bereported. Lower EXPECT thresholds are more stringent, leading to fewerchance matches being reported. Fractional values are acceptable.

The Cutoff score for reporting high-scoring segment pairs is calculatedfrom the EXPECT value. HSPs are reported for a database sequence only ifthe statistical significance ascribed to them is equal to or greaterthat the HSP ascribed to a lone HSP having a score equal to the CUTOFFvalue. Higher CUTOFF values are more stringent, leading to fewer chancematches being reported. Typically, significance thresholds may be moreintuitively managed using EXPECT.

Another function of BLAST is MATRIX. MATRIX is an alternative scoringmatrix for BLASTP, BLASTX, TBLASTN and TBLASTX. The default matrix isBLOSUM62 (Henikoff & Henikoff, 1992). The valid alternative choicesinclude: PAM40, PAM120, PAM250 and IDENTITY. No alternate scoringmatrices are available for BLASTN; specifying the MATRIX directive inBLASTN requests returns an error response. The STRAND function of BLASTrestricts a TBLASTN search to just the top or bottom strand of thedatabase sequences; or restrict a BLASTN, BLASTX or TBLASTX search tojust reading frames on the top or bottom strand of the query sequence.The FILTER function of BLAST is limited to “mask off” segments of thequery sequence that have low compositional complexity, as determined bythe SEG program of Wootton & Federhen (Computers and Chemistry, 1993),or segments having short-periodicity internal repeats, as determined bythe XNU program of Claverie & States (Computers and Chemistry, 1993),or, for BLASTN, by the DUST program. Filtering may eliminatestatistically significant but biologically uninteresting reports fromthe blast output (e.g., hits against common acidic-, basic- orproline-rich regions), leaving the more biologically interesting regionsof the query sequence available for specific matching against databasesequences.

Low complexity sequence found by a filter program is substituted usingthe letter “N” in nucleotide sequence (e.g., “NNNNNNNNNNNNN”) and theletter “X” in protein sequences (e.g., “XXXXXXXXX”). Users may turn offfiltering by using the “Filter” option on the “Advanced options for theBLAST server” page.

Furthermore, filtering is only applied to the query sequence (or, itstranslation products), not to database sequences. Default filtering isDUST for BLASTN, SEG for other programs. It is not unusual, however, fornothing at all to be masked using the filter function of BLAST becausefiltering does not always yield an effect. Furthermore, in some cases,sequences are masked in their entirety, indicating that the statisticalsignificance of any matches reported against the unfiltered querysequence should be suspect.

An alternative database searching engine for use with the presentinvention is another legacy system known as Clustal W. The Clustal Walgorithm is basically the same as for Clustal V. Clustal W improves onthe original Clustal V program, by eliminating terminal gappenalization, thereby treating them the same as all other gaps. Byfreeing the calculation of terminal gaps the alignment is improved byeliminating single residues jumping to the edge of the alignment.

The change in alignment scheme, however, is not without caveats, namelythat a gap near the end of the alignment causes Clustal W to insert agap thereby reducing the alignment score. By freeing terminal gaps,therefore, the overall score of an otherwise good alignment is reduced.In operation, the misalignment may be reduced by lowering the gapopening and reducing the extension penalties. It is difficult, however,to weight the balance between these two functions. The prefilteringfunction of the present invention allows the user to eliminate the needto determine which of the alignment penalties to conform to by reducingthe need to penalize otherwise good alignments. The present inventionallows for maximum specificity and selectivity to be applied topre-screened or filtered sequences.

One great advantage of the Clustal W program is the speed of the initialpairwise alignments. The speed of the alignment in all programs,including BLAST and others, is always commensurate with a decrease inspecificity. Therefore, alignment quality is compromised for speed.Clustal W allows for a slower search speed that increases the accuracyof the alignment. By default, the initial pairwise alignments of ClustalW are carried out using a full dynamic programming algorithm. Thisinitial pairwise alignment is more accurate than the older hash/k-tuplebased alignments (Wilbur and Lipman) but is somewhat slower. On a fastworkstation the difference in speed is often not noted. When searchinglarger and larger databases or clusters of databases, however, theimproved filtering and searching system of the present invention greatlyincreases both accuracy and speed.

Another option of Clustal W is the ability to delay the alignment ofdistant sequences. The user may set a cut-off to delay the alignment ofthe most divergent sequences in a data set until all other sequenceshave been aligned. This delay in distant alignment is particularlyuseful when screening genomic sequences and is important when assessingthe intron/exon junctions and intron repeats across species lines. InClustal W the default is set to 40%, which means that if a sequence isless than 40% identical to any other sequence, its alignment will bedelayed.

Clustal W also allows for the iterative realignment and for resettinggaps between alignments. By default, the alignment of a set sequences asecond time (e.g., with changed gap penalties), causes the gaps from thefirst alignment to be discarded. Discarding the older gaps from previousalignment often provides a better alignments by keeping the gaps (do notreset them) and doing the full multiple alignment a second time.Sometimes, the alignment will converge on a better solution,alternatively, it is possible for the new alignment will be the same asthe first.

Clustal W also allows for sequence profile alignments. By profilealignment, it is meant the alignment of old alignments/ sequences. Inthis context, a profile is just an existing alignment (or even a set ofunaligned sequences). The use of a profile alignment allows the user toread in an old alignment (in any of the allowed input formats) and alignone or more new sequences to that profile. The profile alignment may bea full alignment or a single sequence alignment. In the simplest mode,the user simply aligns the two profiles to each other. Thiscross-profile alignment is useful if to gradually build up a fullmultiple alignment.

A second option is to align the sequences from, e.g., a second profile,one at a time to the first profile. This is done by taking into accountthe underlying sequence comparison tree between the sequences. Thesecond profile alignment is useful if the user has a set of newsequences (not aligned) and wished to add them all to an olderalignment.

Examples of databases that may be used to prescreen for sequencesinclude both public and private databases of either nucleic acid orprotein sequences. As will be understood by those of skill in the art,nucleic acids generally may be either ribonucleic acids ordeoxyribonucleic acids, or derivatives or variants thereof.

One such database is ACEDB. ACEDB is a genome database system developedover the last 7 years primarily by Jean Thierry-Mieg (CNRS, Montpellier)and Richard Durbin (Sanger Centre). It provides a custom databasekernel, with a nonstandard data model designed specifically for handlingscientific data flexibly and a graphical user interface with manyspecific displays and tools for genomic data.

ACEDB may be used for both managing data within genome projects, and formaking genomic data available to other scientists. ACEDB was originallydeveloped for the C. elegans genome project, from which its name wasderived (A C. elegans DataBase). The tools in it have been generalizedto allow for greater flexibility to the point that the same software isnow used for many different genomic databases from, e.g., bacteria,fungi, plants to man. It is also increasingly used for databases withnon-biological content, e.g., vectors and viruses.

The ACEDB software is primarily developed to run under the Unixoperating system, using X-Windows for graphics. Copies of the softwareare accessible via FTP sites, or may be interfaced with through a Webinterface, which serves a number of human databases as well as theAceBrowser system, which serves a local installation of the C. elegansGenome Database.

Referring, to FIG. 1, a block diagram shows some features of the presentinvention. The gene sequence targeting program 100 of the presentinvention comprises a variety of tool types, such as interface tools110, targeting tools 120, analysis tools 130, design tools 140, andcloning tools 150. These tools 110, 120, 130, 140 and 150 are preferablyintegrated together using an objected-oriented programming language.

The interface tools 110 may include a graphical user interface (GUI)112, one or more interfaces with public and private databases 114, anddata storage and output tools 116. The GUI 112 is preferably a menudriven interface that allows a user to jump between applications, pointand click on selections, and view information in graphical form. The oneor more interfaces with public and private databases 114 allow theprogram and the user to access, search and retrieve data from local andremote databases, which may be public or private. These interfaces 114can be configured to allow seamless access to a variety of disparatedatabases, such as publication databases and gene sequence databases.The data storage and output tools 116 may provide access to program helpinformation, experimental documentation features, reports, project datastorage, and data backup, import and export features.

The following sequence comparison software is available from theGenetics Computer Group (GCG) software and may be accessed by the systemof the present invention.

Table I Sequence Retrieval—Interface Tools

Fetch

Copies GCG sequences or data files from the GCG database into yourdirectory or displays them on your terminal screen.

NetFetch

Retrieves entries from NCBI listed in a NetBLAST output file. It canalso be used to retrieve entries individually by entry name or accessionnumber. The output of NetFetch is an RSF file.

The targeting tools 120 allow the user to set the parameters that willbe used to target the gene sequence. These targeting tools 120 mayinclude a phenotypic characteristics selection process 122, a geneprocess 124 and a database selection process 126. The phenotypiccharacteristics selection process 122, gene selection process 124 anddatabase selection process 126 will be described below in more detail inreference to FIGS. 3, 4 and 5 respectively.

The following database searching software is available from the GeneticsComputer Group (GCG) software and may be accessed by the system of thepresent invention.

Table II Database Searching—Targeting Tools Reference Searching

LookUp

Identifies sequence database entries by name, accession number, author,organism, keyword, title, reference, feature, definition, length, ordate. The output is a list of sequences.

StringSearch

Identifies sequences by searching for character patterns such as“globin” or “human” in the sequence documentation.

Names

Identifies GCG® data files and sequence entries by name. It may showwhat set of sequences is implied by any sequence specification.

The analysis tools 130 generate results based on the information andpreferences selected by user with the targeting tools 120 and then allowthe user to analyze those results. The analysis tools 130 may include acomparison and extraction process 132, an alignment process 134 and aprioritizing and filtering process 136. These analysis tools 130 can belegacy systems.

The following analysis tools software is available from the GeneticsComputer Group (GCG) software and may be accessed by the system of thepresent invention.

Table III Multiple Sequence Comparison—Analysis Tools

Gap

Uses the algorithm of Needleman and Wunsch to find the alignment of twocomplete sequences that maximizes the number of matches and minimizesthe number of gaps.

BestFit

Makes an optimal alignment of the best segment of similarity between twosequences optimal alignments are found by inserting gaps to maximize thenumber of matches using the local homology algorithm of Smith andWaterman.

FrameAlign

Creates an optimal alignment of the best segment of similarity (localalignment) between a protein sequence and the codons in all possiblereading frames on a single strand of a nucleotide sequence optimalalignments may include reading frame shifts.

Compare

Compares two protein or nucleic acid sequences and creates a file of thepoints of similarity between them for plotting with DotPlot. Comparefinds the points using either a window/stringency or a word matchcriterion. The word comparison is 1,000 times faster than thewindow/stringency comparison, but somewhat less sensitive.

DotPlot

Makes a dot-plot with the output file from Compare or StemLoop.

GapShow

Displays an alignment by making a graph that shows the distribution ofsimilarities and gaps. The two input sequences should be aligned witheither Gap or BestFit before they are given to GapShow for display.

ProfileGap

Makes an optimal alignment between a profile and one or more sequences.

Pileup

Creates a multiple sequence alignment from a group of related sequencesusing progressive, pairwise alignments. It may also plot a tree showingthe clustering relationships used to create the alignment.

PlotSimilarity

Plots the running average of the similarity among the sequences in amultiple sequence alignment.

MEME

(Multiple EM for Motif Elicitation) Finds motifs in a group of unalignedsequences. MEME saves these motifs as a set of profiles. A databasesearch of sequences with these profiles is then conducted using, e.g.,the Motif Search program.

ProfileMake

Creates a position-specific scoring table, called a profile, thatquantitatively represents the information from a group of alignedsequences. The profile may then be used for database searching(ProfileSearch) or sequence alignment (ProfileGap).

ProfileGay

Makes an optimal alignment between a profile and one or more sequences.

Overlap

Compares two sets of DNA sequences to each other in both orientationsusing a WordSearch style comparison.

NoOverlap

Identifies the places where a group of nucleotide sequences do not shareany common subsequences.

OldDistances

Makes a table of the pairwise similarities within a group of alignedsequences.

Table IV Database Searching—Analysis Tools

Sequence Searching

BLAST

Searches for sequences similar to a query sequence. The query and thedatabase searched may be either peptide or nucleic acid in anycombination. BLAST can search databases on a local computer or databasesmaintained at the National Center for Biotechnology Information (NCBI)in Bethesda, Md., USA.

NetBLAST

Searches for sequences similar to a query sequence. The query and thedatabase searched may be either peptide or nucleic acid in anycombination. NetBLAST can search only databases maintained at theNational Center for Biotechnology Information (NCBI) in Bethesda, Md.,USA.

FastA

Does a Pearson and Lipman search for similarity between a query sequenceand a group of sequences of the same type (nucleic acid or protein). Fornucleotide searches, FastA may be more sensitive than BLAST.

SSearch

Does a rigorous Smith-Waterman search for similarity between a querysequence and a group of sequences of the same type (nucleic acid orprotein). This may be the most sensitive method available for similaritysearches. Compared to BLAST and FastA, it is very slow.

TFastA

Does a Pearson and Lipman search for similarity between a protein querysequence and any group of nucleotide sequences. TFastA translates thenucleotide sequences in all six reading frames before performing thecomparison. It is designed to answer the question, “What implied proteinsequences in a nucleotide sequence database are similar to my proteinsequence?”

TFastX

Does a Pearson and Lipman search for similarity between a protein querysequence and any group of nucleotide sequences, taking frameshifts intoaccount. It is designed to be a replacement for TFastA, and like TFastA,it is designed to answer the question, “What implied protein sequencesin a nucleotide sequence database are similar to my protein sequence?”

FastX

Does a Pearson and Lipman search for similarity between a protein querysequence and any group of nucleotide sequences. TFastA translates thenucleotide sequences in all six reading frames before performing thecomparison. It is designed to answer the question, “What implied proteinsequences in a nucleotide sequence database are similar to my proteinsequence?”

FrameSearch

Searches a group of protein sequences for similarity to one or morenucleotide query sequences, or searches a group of nucleotide sequencesfor similarity to one or more protein query sequences. For each sequencecomparison, the program finds an optimal alignment between the proteinsequence and all possible codons on each strand of the nucleotidesequence optimal alignments may include reading frame shifts.

MotifSearch

Uses a set of profiles (representing similarities within a family ofsequences) as a query to either a) search a database for new sequencessimilar to the original family, or b) annotate the members of theoriginal family with details of the matches between the profiles andeach of the members. Normally, the profiles are created with the programMEME.

ProfileSearch

Uses a profile (representing a group of aligned sequences) as a query tosearch the database for new sequences with similarity to the group. Theprofile is created with the program ProfileMake.

ProfileSegments

Makes optimal alignments showing the segments of similarity found byProfileSearch.

FindPatterns

Identifies sequences that contain short patterns like GAATTC orYRYRYRYR. Patterns may be define ambiguously, thereby allowing for agreater number of mismatches. Patterns may be provided in a file orsimply typed into a terminal.

Motifs

Looks for sequence motifs by searching through proteins for the patternsdefined in the PROSITE® Dictionary of Protein Sites and Patterns. Motifscan display an abstract of the current literature on each of the motifsit finds.

WordSearch

Identifies sequences in the database that share large numbers of commonwords in the same register of comparison with your query sequence. Theoutput of WordSearch can be displayed with Segments.

Segments

Aligns and displays the segments of similarity found by WordSearch.

LineUp

Is a screen editor for editing multiple sequence alignments. Up to 30sequences may be edited simultaneously. New sequences may also be typedin by hand or added from existing sequence files. A consensus sequenceidentifies places where the sequences are in conflict.

Table V Fragment Assembly—Analysis Tools

GelStart

Begins a fragment assembly session by creating a new fragment assemblyproject or by identifying an existing project.

GelEnter

Adds fragment sequences to a fragment assembly project. It acceptssequence data from your terminal keyboard, a digitizer, or existingsequence files.

GelMerge

Aligns the sequences in a fragment assembly project into assembliescalled contigs. The assembled contigs may be viewed and/or edited fromthe assemblies generated in GelAssemble.

GelAssemble

Is a multiple sequence editor for viewing and editing contigs assembledby GelMerge.

GelView

Displays the structure of the contigs in a fragment assembly project.

GelDisassemble

Breaks up the contigs in a fragment assembly project into singlefragments.

Table VI Gene Finding and Pattern Recognition—Analysis Tools

TestCode

Helps you identify protein coding sequences by plotting a measure of thenon-randomness of the composition at every third base. The statisticdoes not require a codon frequency table.

CodonPreference

Is a frame-specific gene finder that tries to recognize protein codingsequences by virtue of the similarity of their codon usage to a codonfrequency table or by the bias of their composition (usually GC) in thethird position of each codon.

Frames

Shows open reading frames for the six translation frames of a DNAsequence. Frames may superimpose the pattern of rare codon choices ifyou provide it with a codon frequency table.

Terminator

Searches for prokaryotic factor-independent RNA polymerase terminatorsaccording to the method of Brendel and Trifonov.

Motifs

Looks for sequence motifs by searching through proteins for the patternsdefined in the PROSITE® Dictionary of Protein Sites and Patterns. Motifscan display an abstract of the current literature on each of the motifsit finds.

MEME

(Multiple EM for Motif Elicitation) Finds conserved motifs in a groupunaligned sequences. MEME saves these motifs as a set of profiles. Adatabase search for sequences with similar profiles may be conductedusing the Motif Search program.

Repeat

Finds direct repeats in sequences. You must set the size, stringency,and range within which the repeat must occur; all the repeats of thatsize or greater are displayed as short alignments.

FindPatterns

Identifies sequences that contain short patterns like GAATTC orYRYRYRYR. The user may define the patterns ambiguously and allowmismatches or provide the patterns in a file or simply type them in fromthe terminal.

Composition

Determines the composition of sequence(s). For nucleotide sequence(s),Composition also determines dinucleotide and trinucleotide content.

CodonFrequency

Tabulates codon usage from sequences and/or existing codon usage tables.The output file is correctly formatted for input to the CodonPreference,Correspond, and Frames programs.

Correspond

Looks for similar patterns of codon usage by comparing codon frequencytables.

Window

Makes a table of the frequencies of different sequence patterns within awindow as it is moved along a sequence. A pattern is any short sequencelike GC or R or ATG. The data output may be plotted with the programStatPlot.

StatPlot

Plots a set of parallel curves from a table of numbers like the tablewritten by the Window program. The statistics in each column of thetable are associated with a position in the analyzed sequence.

FitConsensus

Uses a consensus table written by Consensus as a probe to find the bestexamples of the consensus in a DNA sequence. The number of fits may bespecified by the user and FitConsensus tabulates them with theirposition, frame, and a statistical measure of their quality.

Consensus

Calculates a consensus sequence for a set of pre-aligned short nucleicacid sequences by tabulating the percent of G, A, T, and C for eachposition in the set. FitConsensus uses the Consensus output table as aprobe to search for the best examples of the derived consensus in othernucleotide sequences.

Xnu

Replaces statistically significant tandem repeats in protein sequenceswith X characters. If a resulting protein sequence is used as a queryfor a BLAST search, the regions with X characters are ignored.

Sei

Replaces low complexity regions in protein sequences with X characters.If a resulting protein sequence is used as a query for a BLAST search,the regions with X characters are ignored.

Table VII Protein Analysis—Analysis Tools

Motifs

Looks for sequence motifs by searching through proteins for the patternsdefined in the PROSITE® Dictionary of Protein Sites and Patterns. Motifscan display an abstract of the current literature on each of the motifsit finds.

Profile Scan

Uses a database of profiles to find structural and sequence motifs inprotein sequences.

CoilScan

Locates coiled-coil segments in protein sequences.

HTHScan

Scans protein sequences for the presence of helix-turn-helix motifs,indicative of sequence-specific DNA-binding structures often associatedwith gene regulation.

SPScan

Scans protein sequences for the presence of secretary signal peptides(SPs).

PeptideSort

Shows the peptide fragments from a digest of an amino acid sequence. Itsorts the weight, position, and HPLC retention at pH 2.1. and shows thecomposition of each also prints a summary of the composition of thewhole protein.

Isoelectric

Plots the charge as a function of pH for any peptide sequence.

PeptideMay

Creates a peptide map of an amino acid sequence.

PepPlot

Plots measures of protein secondary structure and hydrophobicity inparallel panels of the same plot.

PeptideStructure

Makes secondary structure predictions for a peptide sequence. Thepredictions include (in addition to alpha, beta, coil, and turn)measures for antigenicity, flexibility, hydrophobicity, and surfaceprobability. Plotstructure displays the predictions graphically.

Plotstructure

Plots the measures of protein secondary structure in the output filefrom PeptideStructure. The measures may be shown on parallel panels of agraph or with a two-dimensional “squiggly” representation.

Mement

Makes a contour plot of the helical hydrophobic moment of a peptidesequence.

HelicalWheel

Plots a peptide sequence as a helical wheel to help you recognizeamphiphilic regions.

Xnu

Replaces statistically significant tandem repeats in protein sequenceswith X characters. If a resulting protein sequence is used as a queryfor a BLAST search, the regions with X characters are ignored.

Seg

Replaces low complexity regions in protein sequences with X characters.If a resulting protein sequence is used as a query for a BLAST search,the regions with X characters are ignored.

The design tools 140 allow the user to select a gene sequence and designdegenerate primers.

The design tools 140 may include a gene sequence selection process 142and a degenerate primer design process 144. The following analysis toolssoftware is available from the Genetics Computer Group (GCG) softwareand may be accessed by the system of the present invention.

Table VIII Primer Selection—Design Tools

Prime

Selects oligonucleotide primers for a template DNA sequence. The primersmay be useful for the polymerase chain reaction (PCR) or for DNAsequencing. Prime allows the user to choose primers from the wholetemplate or limit the choices to a particular set of primers listed in afile.

Table IX Evolution—Design Tools

PAUPSearch

Provides a GCG interface to the tree-searching options in PAUP(Phylogenetic Analysis Using Parsimony). Starting with a set of alignedsequences, a search may be conducted for phylogenetic trees that areoptimal according to parsimony, distance, or maximum likelihoodcriteria; reconstruct a neighbor-joining tree; or perform a bootstrapanalysis.

Distances

Creates a table of the pairwise distances within a group of alignedsequences.

GrowTree

Creates a phylogenetic tree from a distance matrix created by Distancesusing either the UPGMA or neighbor-joining method. A text or graphicsoutput file may be conducted.

Diverge

Estimates the pairwise number of synonymous and non-synonymoussubstitutions per site between two or more aligned nucleic acidsequences that code for proteins.

The cloning tools 150 allow the user to clone genetic material from thedegenerate primers via cloning process 152 as described hereinbelow inthe examples.

Now referring to FIG. 2, a basic flow chart shows a gene sequencetargeting program 200 in accordance with the present invention. The genesequence targeting program 200 begins in block 202. One or morephenotypic characteristics are selected using the phenotypiccharacteristic selection process (see FIG. 3) in block 204. A genesequence that is known to have the selected phenotypic characteristicsis selected using the gene sequence selection process (see FIG. 4) inblock 206. One or more databases containing cataloged gene sequences areselected using the database selection process (see FIG. 5) in block 208.

The selected gene sequence is compared to the cataloged gene sequencesin block 210, and any cataloged gene sequences that contain a portion ofthe selected gene sequence are extracted in block 212. The selected genesequence is aligned to each portion of the extracted gene sequence inblock 214 and the extracted gene sequences are prioritized and filteredbased on the alignment of the selected gene sequence in block 216. Atleast one of the prioritized gene sequences is selected based on one ormore phenotypic criteria in block 218. One or more degenerate primersare designed to target the selected-prioritized gene sequences in block220, and genetic material is cloned using the one or more degenerateprimers in block 222. The program is complete in block 224.

Referring now to FIG. 3, a flow chart shows the phenotypiccharacteristic selection process 204 in accordance with the presentinvention. The phenotypic characteristic selection process 204 begins inblock 302 and a list of available phenotypic characteristics isdisplayed to the user via the GUI 112 (FIG. 1) in block 304. The usercan select one of the displayed phenotypic characteristics, read one ormore phenotypic characteristics from storage, such as a data file, orcreate a new phenotypic characteristic selection option. If the userselects the option of picking one of the displayed phenotypiccharacteristics, as determined in decision block 306, the selectedphenotypic characteristic is read in block 308. The user is thenprompted to select additional phenotypic characteristics in block 310.

If the user selects the option of reading one or more phenotypiccharacteristics from storage, as determined in decision block 306, theuser identifies the location of the stored data in block 314. Thelocation of the stored data may be accessed locally via a disk drive orremotely via a network. The phenotypic characteristics are then readfrom storage in block 316. Standard error handling routines can be usedto report status of the read, operation, test the data, prompt the userfor additional information, or indicate that the read was notsuccessfully completed. The user is then prompted to select additionalphenotypic characteristics in block 310.

If the user selects the option of creating a new phenotypiccharacteristic selection option, as determined in decision block 306,the new phenotypic characteristic data is read in block 318. This newdata can be entered directly by the user or read from a file. The newphenotypic characteristic data is stored in block 320 and can beincluded in the list of available phenotypic characteristics displayedin block 304. If the new phenotypic characteristic data has errors orwas not properly read and stored, as determined in decision block 322,the error is reported in block 324. If a maximum number of retryattempts has not occurred, as determined in decision block 326, the newcharacteristic process repeats by again reading the new phenotypiccharacteristic data in block 318. If, however, there are no errors, asdetermined in decision block 322, or the maximum number of retryattempts has occurred, as determined in decision block 326, the user isprompted to select additional phenotypic characteristics in block 310.

After the selected method is complete (see blocks 308, 316, 322 and326), the user may then elect to select additional phenotypiccharacteristics. If the user elects to select additional phenotypiccharacteristics, as determined in is decision block 310, the list ofavailable phenotypic characteristics is displayed again in block 304 andthe process repeats as previously described. If, however, the userelects to not select additional phenotypic characteristics, asdetermined in decision block 310, processing returns to the main programin block 312.

Now referring to FIG. 4, a flow chart shows the gene sequence selectionprocess 206 in accordance with the present invention. The gene selectionprocess 206 begins in block 402. The user can enter a gene sequenceusing the GUI, read a gene sequence from storage, such as a data file,or search for all or part of a gene sequence. If the user selects theoption of entering a gene sequence using the GUI, as determined indecision block 404, the gene sequence is read in block 406 andprocessing returns to the main program in block 408.

If the user selects the option of reading a gene sequence from storage,as determined in decision block 404, the user identifies the location ofthe stored data in block 410. The location of the stored data may beaccessed locally via a disk drive or remotely via a network. The genesequence is then read from storage in block 412 and processing returnsto the main program in block 408. Standard error handling routines canbe used to report status of the read operation, test the data, promptthe user for additional information, or indicate that the read was notsuccessfully completed.

If the user selects the option of searching for all or part of a genesequence, as determined in decision block 404, the search parameters,such as the database to be searched, are defined in block 414. Thesearch is performed in block 416. If a gene sequence was not found, asdetermined in decision block 418, the user is again prompted to select agene sequence selection method in block 404. If, however, a genesequence was found, as determined in decision block 418, the searchresults are displayed in block 420. The user can then run a new search,save the search results, select a gene sequence from the search resultsor exit the selection process. If the user elects to run a new search,as determined in decision block 422, processing returns to block 414where the search parameters are again defined. If the user elects tosave the search results, as determined in decision block 422, the searchresults are then save to storage in block 424 and the user can then runa new search, save the search results, select a gene sequence from thesearch results or exit the selection process. If the user elects toselect a gene sequence from the search results, as determined indecision block 422, the gene sequence is selected in block 426 and theuser can then run a new search, save the search results, select a genesequence from the search results or exit the selection process. If theuser elects to exit the process, as determined in decision block 422,processing returns to the main program in block 408.

Referring now to FIG. 5, a flow chart shows the database selectionprocess 208 in accordance with the present invention. The databaseselection process 208 begins in block 502 and a list of availabledatabases is displayed to the user via the GUI 112 (FIG. 1) in block504. The user can select one of the displayed databases, or provide thenecessary information to search a new database. If the user selects theoption of picking one of the displayed databases, as determined indecision block 305, the database selection is read in block 508. A listof available superfamilies, families and subfamilies for the selecteddatabase is displayed in block 510 and the family selection is read inblock 512. The user is then prompted to select additional databases inblock 514.

If the user selects the option of providing the necessary information tosearch a new database, as determined in decision block 506, the datanecessary to read the new database is read in block 518. This new datacan be entered directly by the user or read from a file. The newdatabase information is stored in block 520 and can be included in theis list of available databases displayed in block 504. If the newdatabase information has errors or was not properly read and stored, asdetermined in decision block 522, the error is reported in block 524. Ifa maximum number of retry attempts has not occurred, as determined indecision block 526, the new database process repeats by again readingthe information necessary to search the new database in block 518, if,however, there are no errors, as determined in decision block 522, orthe maximum number of retry attempts has occurred, as determined indecision block 526, the user is prompted to select additional databasesin block 514.

After the selected method is complete (see blocks 512, 522 and 526), theuser may then elect to select additional databases. If the user electsto select additional databases, as determined in decision block 514, thelist of available databases is displayed again in block 504 and theprocess repeats as previously described. If, however, the user elects tonot select additional databases, as determined in decision block 514,processing returns to the main program in block 516.

It should be understood that all of the above processes are capable ofbeing executed either on a single computer, or via a coordinatingnetwork of computers, each of which is capable of executing any of thedescribed processes. It should further be understood that the inventionset forth herein may be stored within computer memory, or on a harddrive or multiple hard drives of one or more computers, server or othermedia, e.g., CD-ROM or diskette.

A system of data mining tools has been developed to help identify,isolate and clone biologically and functionally important genes frompublic genomic libraries. The software suite called SPADE™, is designedto seamlessly integrate available search and analysis tools so thatcomputer experiments for sequence analysis can be quickly designed andexecuted and that rational primer design, cloning and proteincharacterization can be accomplished.

SPADE™ is a client/server application. The clients interact with theserver, which can be a dedicated LINUX server, via a local area networkor a web interface. Therefore, the interaction is platform-free. Anexample of the system network overview is illustrated in FIG. 6.

An illustration of the main program flow is exemplified in FIG. 7. Auser first logs in and is the presented with a main menu. The main menupresents four choices: Database Management (FIG. 8), WorkspaceManagement (FIG. 9), Search Tools and Analysis Tools (FIG. 10). TheDatabase Management screen allows the administrator of the system toconfigure the local genomic databases associated with SPADE™. In thisscreen, there is a list of current databases online, a button to editthe configuration for each individual database, and options to add newdatabases or delete existing databases. The Workspace Management screenallows the user to access his or her data, files and documentation onthe server. It is similar to a file management program. There is a listof projects, and the files in the current project. The user can open aproject, create new projects or delete existing projects. Within eachproject, the user can open individual data files, rename, delete, uploador download files. The search tool screen allows the user to searchdatabases with the algorithms associated with SPADE™. The user firstselects the database via a database selection window, and then selectsthe sequence to search from the project files or enters the sequencedirectly into the text box. The user then selects the algorithm tosearch, and accepts the default parameters or modifies the appropriateparameters. Users can access the advance parameters via the advanceparameters screen. Finally, the server executes the search and returnsthe result to the user. The search tool screen also allows the user toanalyze the results of the previous search or analysis with thealgorithms associated with SPADE™. The user first selects the sequenceto analyze from the project files or enters the sequence directly intothe text box. The user then selects the, algorithm to execute, andaccepts the default parameters or modifies the appropriate parameters.Users can access the advance parameters via the advance parametersscreen. Finally, the server executes the algorithm and returns theresult to the user.

An example of the system architecture overview is illustrated in FIG.11, showing the interaction of the platform-free users with the fourscreens discussed above. FIG. 12 describes a use of the system describedin FIG. 11.

The seamless integration of the various components described in theprocess flow discussed above, allows for the modification of existingcomponents and the introduction of additional components whichfacilitate the characterization, targeting, cloning, validation, searchand analysis, sorting, indexing, cataloging and conversion of variousforms and formats of data and databases including, but not limited to,DNA sequences, amino acid sequences, DNA and protein motifs, images,patterns, and tertiary and quaternary structure including, atomic andmolecular-level interactions. Therefore, the system described above maybe used to perform high throughput database conversion, high specificityand high throughput selection of primers, as well as high specificityand high throughput positioning of protein and DNA structure and motifs.In addition, each of the various components described in the processflow discussed above may be used individually or in combination with theremaining components, thereby allowing for the delivery of results froman individual component or a combination of components, as desired.

EXAMPLE 1 Isolation of Nucleic Acid Molecules Related to Integrin

The integrin family of cell adhesion receptors plays a fundamental rolein the processes involved in cell division, differentiation andmovement. The extracellular domains of integrin alpha/beta heterodimersmediate cell-matrix and cell-cell contacts while their cytoplasmic tailsassociate with the cytoskeleton and integrins can transduce informationbidirectionally. Studies have led to the identification of theligand-binding region on the beta subunit and sequences in thecytoplasmic tails of the beta subunits that interact with cytoskeletaland signaling components. Green, L. J., et al., The integrin betasubunit, Int J Biochem Cell Biol (1998) 30(2):179-184. Integrin beta 1(ITGB1) is a subunit of type I membrane proteins and has cysteine richdomains that are involved in intrachain disulfide bonds. It associateswith the alpha-1 or alpha-6 subunits to form a laminini receptor, withalpha-2 to form a collagen receptor, with alpha-4 to interact withvcam-1, with alpha-5 to form a fibronectin receptor and with alpha-8.

In order to demonstrate the system and method for identifying functionalproteins in other target organisms, an integrin-like molecule mostclosely related to integrin beta 1 was identified and cloned fromManduca sexta (M. sexta). In this example, the original phenotypiccharacteristics selected were that the target molecule include aspecific function and tissue localization. The specific functionidentified was that the target be an integral membrane protein involvedin cytoskeletal formation. The localization selected was that theprotein be expressed in the midgut of an organism.

These structural-functional parameters were then used to targetpotential genes based on the function identified from the PubMeddatabase on all organisms (see FIG. 2). That is, the original search fora protein was not restricted by filtering.

Following the initial identification of a target and the filtering ofsequences, an alignment of the beta integrin proteins that wereidentified from all organisms was conducted and primer selection wasmade based on the identified matching sequences between the differentorganisms. The primer design software was the MacVector software, andfollowing an initial round of sequence determination, the primer designwas improved.

RT-PCR was conducted from M. sexta mRNA and following the PCR reaction aband of the expected size was cut out of a low-melt agarose gel. The PCRproducts were then cloned into the pAT vector and inserts sequenced. ABLAST alignment of the sequences indentified a clone with similarity toPacifastacus leniusculus (signal crayfish), Drosophila (fruit fly),Anopheles gambiae (African malaria mosquito) integrin beta 1 sequences.

The insert from these clones was then used to clone the full-length cDNAfrom a M. sexta library.

EXAMPLE 2 Isolation of a Known Gene to Validate System

In order to validate the system, it was used to isolate a known gene; inthis case the M. sexta inopeptidase gene. Aminopeptidase is involved inthe modulation of various cellular responses, especially in cell-celladhesion and signal transduction. We are particularly interested inaminopeptidase because we have shown that it is directly involved inresistance by insects to insecticidal toxins of Bacillus thuringiensis.We believe that it is a major factor involved in innate immunity ofinvertebrate and vertebrate epithelial cells. The M. sextaaminopeptidase gene was mined based on nucleotide and amino acidsequence alignment with the existing aminopeptidase related sequences,excluding the tobacco hornworm sequences. The primers used for PCR werebased on such alignment.

Using this method, the tobacco hornworm aminopeptidase gene has beenpartially cloned and sequenced (not shown). The amino acid sequencefragments showed high homology (99-100%) to GenBank Acc. No. P91885(Denolf, P., et al., Cloning and characterization of Manduca sexta andPlutella xylostella midgut aminopeptidase N enzymes related to Bacillusthuringiensis toxin-binding proteins, Eur. J. Biochem. (1997)248(3):748-761). Thus, the gene mining technique has been proven toisolate a known gene.

EXAMPLE 3 Future Experiments

The above insect genes will be further characterized according to wellestablished methods. Protein and peptide antibodies are made accordingto established protocols. The antibodies are used to confirm tissue andcellular localization of the expressed protein. The extent of homologyof the identified genes with other insect species and other genera ischecked by zooblot at varying hybridization stringencies. Therecombinant proteins are expressed, in for example, insect SF9 cells,and purified using the above antibodies, by GST or HIS tagimmunoaffinity or by other means known in the art. The genes are mutatedto prepare truncation mutants in order to delineate the boundaries ofthe functional proteins.

While this invention has been described in reference to illustrativeembodiments, this description is not intended to be construed in alimiting sense. Various modifications and combinations of theillustrative embodiments, as well as other embodiments of the invention,will be apparent to persons skilled in the art upon reference to thedescription. It is therefore intended that the appended claims encompassany such modifications or embodiments.

1. A method to design primers which identify and target a firstnucleotide sequence derived from a first species wherein the expressionof said first nucleotide sequence results in at least one phenotypiccharacteristic, the method comprising the steps of: providing a secondnucleotide sequence derived from a second species different from thefirst species that is known to result in the phenotypic characteristic;comparing the second nucleotide sequence to nucleotide sequencescataloged in one or more databases that annotate nucleotide sequenceswith phenotypic characteristics; extracting any cataloged nucleotidesequences that contain a portion of the second nucleotide sequence andwhich are annotated with said phenotypic characteristic; aligning thesecond nucleotide sequence to each extracted nucleotide sequence;analyzing the extracted nucleotide sequences based on at least percentsimilarity to find portions of said sequences having the highest percentsimilarity; and designing one or more primers based on said portions ofthe aligned sequences, which primers identify and target said firstnucleotide sequence.
 2. The method of claim 1, further comprising a stepprior to the step of extracting cataloged nucleotide sequences offiltering the second nucleotide sequence to eliminate portions which areregions commonly found in encoding nucleotide sequences.
 3. The methodof claim 1, further comprising a step of cloning said first nucleotidesequence using the one or more designed primers.
 4. The method of claim1, wherein the one or more databases are selected from databasescomprising catalogued gene sequences for humans, rats, mice, zebra fish,frogs, Drosophila, nematode, C. elegans, mosquito and bacteria.
 5. Themethod of claim 1, wherein the one or more designed primers are nested.6. The method of claim 1, wherein the second nucleotide sequence isaligned to each extracted nucleotide sequence by comparing deduced aminoacid sequences.
 7. The method of claim 1, wherein the second nucleotidesequence is aligned to each extracted nucleotide sequence by comparingthe nucleotide sequences.
 8. The method of claim 1, wherein thephenotypic characteristic is expression in insect mid-gut epithelium. 9.A system for designing primers which primers target a first nucleotidesequence wherein the expression of said first nucleotide sequenceresults in at least one phenotypic characteristic comprising: one ormore computers collectively having program means thereon for performingthe method of claim 1; and one or more databases containing thecataloged gene sequences; and a communication link connecting thecomputer or computers to said one or more databases.
 10. The system ofclaim 9, wherein the program means comprises instructions for filteringthe second nucleotide sequence to eliminate portions which are regionscommonly found in encoding nucleotide sequences.
 11. The system of claim10, wherein the one or more databases are selected from databasescomprising cataloged sequences for humans, rats, mice, zebra fish,frogs, Drosophila, nematode, C. elegans, mosquito and bacteria.
 12. Thesystem of claim 9, wherein the phenotypic characteristic is expressionin insect mid-gut epithelium.
 13. The system of claim 9, wherein the oneor more designed primers are nested.
 14. The system of claim 9, whereinthe selected gene sequence is aligned to each extracted nucleotidesequence by comparing deduced amino acid sequences.
 15. The system ofclaim 9, wherein the second nucleotide sequence is aligned to eachextracted gene sequence by comparing nucleotide sequences.
 16. Acomputer system embodied on a computer-readable medium for designingprimers to identify and target a first nucleotide sequence derived froma first species wherein the expression of said first nucleotide sequenceresults in at least one phenotypic characteristic, said computer systemcomprising: means for providing a second nucleotide sequence derivedfrom a second species different from the first species that results inthe phenotypic characteristic; means for providing at least one databasecontaining nucleotide sequences cataloged therein wherein said catalogannotates said sequences to resulting phenotypic characteristics; meansfor extracting from said at least one database a plurality of catalogednucleotide sequences containing a portion of the said second nucleotidesequence and which are annotated with said phenotypic characteristic;means for aligning said second nucleotide sequence with said catalogedgene sequences; means for analyzing the extracted gene sequences basedon at least percent similarity to find portions of said sequences havingthe highest percent similarity; and means for designing one or moreprimers based on said portions of the aligned prioritized sequences,which primers identify and target said first nucleotide sequence. 17.The computer system of claim 16, further comprising a code segment forfiltering the second nucleotide sequence to eliminate portions which areregions commonly found in encoding nucleotide sequences.
 18. Thecomputer program of claim 16, wherein the one or more databases areselected from databases comprising cataloged gene sequences for humans,rats, mice, zebra fish, frogs, Drosophila, nematode, C. elegans,mosquito and bacteria.
 19. The computer system of claim 16, wherein thephenotypic characteristic is expression in insect mid-gut epithelium.20. The computer system of claim 16, wherein the one or more designedprimers are nested.
 21. The computer system of claim 16, wherein themeans for aligning the second nucleotide sequences aligns each extractednucleotide sequence by comparing deduced amino acid sequences.
 22. Thecomputer system of claim 16, wherein the second nucleotide sequence isaligned to each extracted gene sequence by comparing nucleotidesequences.